The once flat region South of Johannesburg is littered with man-made yellow hillocks. These hills are a reminder of the history of Johannesburg – a city literally built on gold. The gold has been mined here since the rush of the 1880s.
Much of the gold was extracted from the crushed rock and the left-overs transported by mules and later trucks to these locations. However, the mine-dumps (as they’re locally known) are yellow-coloured as a reminder that even after the original extract – allusive gold still exists in the waste piles.
A mine-dump in Elsburg has an estimated weight of 140 million tonnes. It is estimated that within the dump are specks of gold totalling 1.3 Million ounces (valued at over a billion dollars). It is only in the last few years that we have developed the technology to recycle the mine-dumps to extract nearly 50% of the remaining gold.
The story of the mine-dumps provides an apt analogy to the data world of today. Whether you’re in the banking, telecommunications, retail or insurance sectors, you have invariably witnessed the amassing of large amounts of data. Similar to a mine-dump, this data is speckled with valuable information. The conundrum is how to identify the valuable pieces and how to harness the value. In essence the goal is to convert your transaction data to information, then to knowledge and finally to wisdom.
This is also the foundation of an early approach to dealing with Big Data analysis.
How to understand your Big Data
Large data sets are growing at an ever increasing rate. Credit, loyalty and marketing managers recognise that there’s likely value in this data, but the question we hear regularly is, where to start?
In this post, I’d like to propose a simple, but achievable approach. There are more advanced approaches that I’ll explore in future postings.
The process can be broken up into manageable parts incorporating the 3Ds: Determination, Development and Deployment.
1. The Determination phase
Incorporating the data
Aggregating the data
Identifying the target base areas of value
Scouring the data for value
Reviewing and planning mini-projects
2. The Development phase
Developing models (e.g. scorecards and decision trees) using valuable data for pre-determined outcomes
3. The Deployment phase
Setting up a process
Deployment of aggregation
Deployment of models
Deployment of treatments
Continual measurement and adjustment
This approach has helped organisations worldwide to find and harness the glittering nuggets of value in their vast and ever-expanding dumps of data.
Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.